The role of a rapid diagnostic test to diagnose group A streptococcal pharyngitis in a paediatric emergency department: a diagnostic accuracy study

Author(s):  
Gemma Winzor
2020 ◽  
Vol Volume 15 ◽  
pp. 645-654 ◽  
Author(s):  
Simone Chantal Gafner ◽  
Caroline Henrice Germaine Bastiaenen ◽  
Serge Ferrari ◽  
Gabriel Gold ◽  
Andrea Trombetti ◽  
...  

PEDIATRICS ◽  
1989 ◽  
Vol 83 (5) ◽  
pp. 808-808
Author(s):  
JENNIFER S. READ ◽  
ROBERT H. BEEKMAN

Redd and co-workers found the sensitivity of their rapid diagnostic test for group A streptococcal pharyngitis to be 62.8% and its specificity to be 96.9%. Furthermore, the positive predictive value of the test was determined to be 91.5%, sufficiently high to significantly influence the care provided to their patients. We strongly disagree with the authors' conclusion that their findings can be extrapolated to the general pediatric setting. Bayes theorem clearly relates a test's positive predictive value to its sensitivity as well as to the prevalence of true disease in the population.


2014 ◽  
Vol 60 (2) ◽  
pp. 267-270 ◽  
Author(s):  
R. Cohen ◽  
C. Levy ◽  
S. Bonacorsi ◽  
A. Wollner ◽  
M. Koskas ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Athanasios Pavlou ◽  
Robert M. Kurtz ◽  
Jae W. Song

Accuracy is an important parameter of a diagnostic test. Studies that attempt to determine a test’s accuracy can suffer from various forms of bias. As radiology is a diagnostic specialty, many radiologists may design a diagnostic accuracy study or review one to understand how it may apply to their practice. Radiologists also frequently serve as consultants to other physicians regarding the selection of the most appropriate diagnostic exams. In these roles, understanding how to critically appraise the literature is important for all radiologists. The purpose of this review is to provide a framework for evaluating potential sources of study design biases that are found in diagnostic accuracy studies and to explain their impact on sensitivity and specificity estimates. To help the reader understand these biases, we also present examples from the radiology literature.


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